DESCRIPTION (Applicant's Abstract): Children are amazing word learners. Even though the number of possible meanings for each novel word is immense, children learn words quickly and with seemingly little effort. Previous research has suggested that children are biased to only consider some of the possible meanings for a new word. However, the origin of word learning biases and the mechanisms by which they operate on a moment-to-moment basis has yet to be determined. The proposed project addresses this gap in our understanding of word learning. In three projects, I will test the hypothesis that word learning biases develop out of statistical regularities in the language and categories children learn. The specific questions addressed are: (1) what are the statistical regularities in the language input of children; (2) can a simple learner of statistical regularities replicate the development of specific word-learning biases; and (3) if the statistical regularities found in the words children know are altered, does the developmental trajectory of specific word learning biases by change? These questions are addressed in neural network simulations of the development of specific word learning biases; experimental studies in which the natural development of a word learning bias is altered by teaching children words; and in modeling of the changes in word learning that arise from the lexical training.